Inferring mitochondrial and cytosolic metabolism by coupling isotope tracing and deconvolution

The inability to inspect metabolic activities within distinct subcellular compartments has been a major barrier to our understanding of eukaryotic cell metabolism. Previous work addressed this challenge by analyzing metabolism in isolated organelles, which grossly bias metabolic activity. Here, we describe a method for inferring physiological metabolic fluxes and metabolite concentrations in mitochondria and cytosol based on isotope tracing experiments performed with intact cells. This is made possible by computational deconvolution of metabolite isotopic labeling patterns and concentrations into cytosolic and mitochondrial counterparts, coupled with metabolic and thermodynamic modelling. Our approach lowers the uncertainty regarding compartmentalized fluxes and concentrations by one and three orders of magnitude compared to existing modelling approaches, respectively. We derive a quantitative view of mitochondrial and cytosolic metabolic activities in central carbon metabolism across cultured cell lines without performing cell fractionation, finding major variability in compartmentalized malate-aspartate shuttle fluxes. We expect our approach for inferring metabolism at a subcellular resolution to be instrumental for a variety of studies of metabolic dysfunction in human disease and for bioengineering.


Figure S1 :
Figure S1: Determining flux directionalities via CODE-MFA.(a) Illustration of the number of reactions whose flux is uniquely determined throughout the different steps of CODE-MFA.(b) Number of reactions whose direction of net flux is uniquely inferred across CODE-MFA iterations in HeLa cells.

Figure S3 :
Figure S3: Gene expression levels obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE), with 0.5 RPKM value as the cutoff to determine non-expressed genes.

Figure S4 :
Figure S4: Correlation between gene expression and enzyme abundance levels with most probable net flux inferred by CODE-MFA in HeLa cells.(a) Gene expression levels and most probable net flux inferred by CODE-MFA through the corresponding enzyme.(b) Gene expression levels and most probable net flux inferred by MFA through the corresponding enzyme.(c) Measured enzyme abundance 47 and most probable net fluxes inferred by CODE-MFA (d) Measured enzyme abundance 88 and most probable net fluxes inferred by MFA

Figure S6 :
Figure S6: CODE-MFA performance in inferring compartmentalized fluxes in HCT116, A549, and LN229.(a,b,c) Percentage of reactions in the model whose direction of net flux is inferred by CODE-MFA versus with MFA and MFA without thermodynamics considerations in HCT116 (a), A549 (b), and LN229 (c).(d,e,f) Cumulative distribution of reaction net flux confidence interval sizes inferred by CODE-MFA versus MFA and CoDe-MFA without thermodynamic considerations in HCT116 (d), A549 (e), and LN229 (f).

Figure S7 :
Figure S7: CODE-MFA performance in inferring compartmentalized metabolite concentrations and Gibbs free energy in HCT116, A549, and LN229.(a,b,c) Cumulative distribution of reaction metabolite concentration confidence interval sizes inferred by CODE-MFA versus with strictly thermodynamic analysis in HCT116 (a), A549 (b), and LN229 (c).(d,e,f) Cumulative distribution of reaction Gibbs energy confidence interval sizes inferred by CODE-MFA versus with strictly thermodynamic analysis in HCT116 (d), A549 (e), and LN229 (f).